ReloQate: Transient Drift Detection and In-Situ Recalibration in Surface Code Quantum Error Correction

TL;DR

This paper introduces a real-time method for predicting logical error rates in surface code quantum error correction by using detector fire rate data, enabling dynamic error mitigation through logical qubit remapping.

Contribution

It presents a novel approach to predict logical error rates in real time using detector fire rates, and pairs this with a remapping scheme to mitigate error drift in quantum hardware.

Findings

Detector fire rate-based prediction effectively estimates logical error rates.

Remapping logical qubits to fresh tiles mitigates error drift efficiently.

The method is adaptable to other stabilizer codes.

Abstract

Quantum error correction (QEC) promises to exponentially suppress qubit noise, but typically assumes spatially-uniform and temporally-constant noise rates. However, real quantum hardware exhibits variation in noise levels over time, which will be amplified by QEC if not addressed. To mitigate this drift in error rates, we leverage transient information readily available in surface code quantum error correction to predict logical error rates (LER) in real time. We infer a prediction model by sampling physical error rates from real hardware, and mapping detector fire rate (DFR), or parity of stabilizer measurements across QEC rounds, to LER. This allows for on-the-fly LER predictions without the typical characterization overhead required to determine LER. This method can easily be extended to other stabilizer codes. Importantly, we observe that this prediction should be accurate yet conservative (i.e. give an upper estimate) to enable appropriately fast responses to real-time physical error changes. That is, responses should be executed marginally ahead of time to allow for their execution to complete, and minimize time spent (ideally none) above intolerable error rates. More importantly, we pair this predictor with a scheme which remaps drifted logical qubits to fresh tiles in a patch-based architecture while their original tiles are recalibrated. Our results demonstrate DFR-based prediction to be an effective LER predictor, and remapping as a spatially efficient and timely mitigation response for small code distances, both of which are significant steps in furthering practical QEC.